1. Field of the Invention
The field of the present invention generally relates to network management and, more particularly, to systems and methods for monitoring, simulating, and enhancing network performance.
2. Background
Networks and networked applications are difficult to operate at peak efficiency for a variety of reasons. First, the heterogeneous nature of most modern networks often requires that dissimilar or diverse network components interact with one another, leading to potential inefficiencies in overall operation of the network, even if the operation of a sub-network portion or individual network elements have been optimized. Heterogeneity can exist in one or more network or application characteristics including bandwidth, latency, media, and traffic types.
A second reason for inefficiencies in network operations is that the total traffic volume and/or the mix of different traffic types (e.g., data, image, streaming video) may vary in an unpredictable manner. Identifying a static network configuration that will offer efficient performance in the face of dynamically changing traffic and network conditions can be particularly challenging.
A third factor contributing to operational inefficiencies is the tendency of networks to grow over time. As networks or networked applications grow in scale, a network configuration that may have been optimal or sufficient when the network or application was smaller may no longer be so when the network or application becomes larger in size.
A fourth reason for performance inefficiencies in networks is the need for shared access to network resources. For example, a user communicating with a wireless transmission device may need to share the spectrum with other wireless users. Similarly, a user transmitting large files over a company's intranet may need to share the available bandwidth with users engaged in other activities—for example, a second user engaged in a video teleconference, and a third user sending e-mail. Where a network or application is required to share resources with other users or systems, the available network resources may vary both dynamically and unpredictably. Consequently, a network configuration that is optimal or sufficient when shared resources are not being used by others, or are only lightly used by others, may not be optimal or sufficient when outside users or systems place greater demand upon the shared resources.
To improve the performance and efficiency of networks and/or networked applications, simulation tools have been developed, whereby traffic flow can be modeled and various network configurations explored. Most, if not all, conventional simulation tools, however, rely upon off-line analysis of the network and its traffic load, and/or are quite limited in accuracy, speed or capability.
A conceptual illustration of typical off-line simulation and analysis as conventionally practiced in a simulation process 100 is shown in FIG. 1. Generally in off-line analysis, a network model 113 is constructed to simulate operation of the network. Information about traffic carried over the network is gathered to determine, for example, appropriate link sizes and optimal routes that will provide a predetermined level of service for a forecasted traffic load. The traffic load may be forecasted using traffic models 114 that perform statistical analysis of the collected traffic data. The forecasted traffic load is input to a simulation software program 120 that models the network and forecasted traffic. The network model 113 and traffic model 114 are used in conjunction to evaluate alternative system configurations. Typically, the outputs from simulations with various proposed network configurations and/or forecasted traffic patterns are reviewed manually, and then adjustments to the proposed network configuration and/or forecasted traffic patterns can be made in an attempt to arrive at a suitable network configuration for the expected traffic load. When a suitable network configuration is found, based upon manual review of the simulation results, the new configuration then may, if desired, be deployed (indicated in FIG. 1 by block 125).
While off-line simulation and analysis provides some measure of prediction for network performance, it has a number of drawbacks. First, off-line simulation and analysis can be a resource intensive, time-consuming process. Conventional approaches for off-line simulation and analysis can easily take hours or even days to complete, particularly for large-scale or complex networks. Consequently, the results of the off-line simulation and analysis may be “stale” by the time the process is completed, and may not reflect the actual status of the network or its traffic load at the time that the computations are completed.
In order to increase the speed of off-line network simulations, the network model may be simplified by the incorporation of abstractions or other simplifications, thus reducing the scope of the simulation and, consequently, the computational burden. However, the use of abstractions and simulations in the network model can adversely affect the reliability and accuracy of the simulation results. For maximum reliability and accuracy, networks should preferably be simulated without abstracting system properties that may affect overall system performance. These concerns are particularly prevalent when the simulation objectives include the investigation of scalability and/or stability of the target network. An overly simplified network model can lead to inaccurate predictions or estimations of network attributes that the analyst is trying to estimate. The problem of obtaining accurate and reliable network simulation results is expected to increase with conventional simulation and analysis techniques as existing networks are integrated into larger communication systems, resulting in large-scale, integrated networks having numerous co-existing heterogeneous nodes with potentially diverse sets of protocols and services operated within the integrated network. Performance of a network may be impacted in a variety of ways by its integration into a larger system, which may lead to catastrophic problems that cannot be predicted by analyzing the individual (sub) networks by themselves.
One example of a method that attempts to enhance the performance of network operation is described in U.S. Pat. No. 6,069,894, in the name of Holendar et al. According to the technique described in that patent, a number of logical networks are established on top of a physical network, the logical links of the logical networks sharing the same physical transmission and switching resources. Using an analytical model of the network, an “objective function” is optimized with respect to at least one set of decision variables (generally either the logical link capacities and/or the load sharing variables), given the physical network parameters and the requirements of each logical network.
Techniques which rely upon an analytical model for simulation may not capture the full operation of the system, and may therefore lead to sub-optimal results. Analytical models may also lack the ability to simulate detailed behavior of certain individual components, such as routers and switches, that are commonly used in many actual networks. Moreover, it is largely understood that the use of analytical models impose upon the simulation a number of limitations or assumptions necessary for analytical tractability (for example, a reduced load, link independence, etc.).
Another example of a method addressed to enhancing network performance is described in U.S. Pat. No. 6,209,033, in the name of Datta et al. In that patent, a system is described for evaluating and planning network capacity based upon measured traffic across the links of the network. Once a link's traffic volume has been measured, it is compared with the link's traffic capability, and the resulting parameters compared with the traffic and capability of other links of the network to create metrics of the network capacity and balance. Then, simulated changes to the network configuration may be made by substituting simulated traffic volume amount and capabilities for selected link traffic measurements and capabilities, and the resulting measures of network capacity and balance are then compared to determine whether the simulated changes represent a preferred network configuration. While addressing certain limited aspects of network performance, the technique described in the patent focuses mainly on achieving overall balance in the network (where all link utilizations are approximately equal), and does not generally address itself to other network attributes that might, for example, rely upon detailed models of routers and/or other network devices. Moreover, the system described in the patent uses sequential discrete event simulation, which may limit its capabilities or its potential applications to other network scenarios.
Wireless communication systems, in particular, may present difficult challenges for simulation, analysis and optimization. A primary source of these difficulties is the accurate computation of the impact of interference among simultaneously transmitted wireless signals. Much of the previous work has relied on the use of abstract models of dubious practical value because they are generally based on unrealistic assumptions of interference between simultaneously transmitted signals. However, the ability to model, simulate, or otherwise understand the effect of signal interference, and interference reduction techniques (such as power management in a Code Division Multiple Access (CDMA) communication network), can be quite important, particularly as networks grow and must deal with larger numbers of users.
There is thus a need for a system and/or method for improving network efficiency and, in particular, such a system and/or method that may be applied to wired, wireless, and/or mixed networks with large numbers of network nodes. There is likewise a need for a system and/or method for improving network efficiency which is well suited for the expected large volume of growth in communication services, and which is well suited for providing differentiated services that can allow the network to, for example, provide continuous unaffected service for premium customers, even as the overall offered traffic load increases substantially.